OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Network Cartography
Venue: arXiv cs.NI 2012
Year: 2012

Communication networks have evolved from specialized, research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer-oriented, and heterogeneous. Propelled by emergent social networking services and high-definition streaming platforms, network traffic has grown explosively thanks to the advances in processing speed and storage capacity of state-of-the-art communication technologies. As "netizens" demand a seamless networking experience that entails not only higher speeds, but also resilience and robustness to failures and malicious cyber-attacks, ample opportunities for signal processing (SP) research arise. The vision is for ubiquitous smart network devices to enable data-driven statistical learning algorithms for distributed, robust, and online network operation and management, adaptable to the dynamically-evolving network landscape with minimal need for human intervention. The present paper aims at delineating the analytical background and the relevance of SP tools to dynamic network monitoring, introducing the SP readership to the concept of dynamic network cartography -- a framework to construct maps of the dynamic network state in an efficient and scalable manner tailored to large-scale heterogeneous networks.
Investigating Effects of Common Spreadsheet Design Practices on Correctness and Maintainability
Venue: arXiv cs.SE 2012
Year: 2012

Spreadsheets are software programs which are typically created by end-users and often used for business-critical tasks. Many studies indicate that errors in spreadsheets are very common. Thus, a number of vendors offer auditing tools which promise to detect errors by checking spreadsheets against so-called Best Practices such as "Don't put constants in fomulae". Unfortunately, it is largely unknown which Best Practices have which actual effects on which spreadsheet quality aspects in which settings. We have conducted a controlled experiment with 42 subjects to investigate the question whether observance of three commonly suggested Best Practices is correlated with desired positive effects regarding correctness and maintainability: "Do not put constants in formulae", "keep formula complexity low" and "refer to the left and above". The experiment was carried out in two phases which covered the creation of new and the modification of existing spreadsheets. It was evaluated using a novel construction kit for spreadsheet auditing tools called Spreadsheet Inspection Framework. The experiment produced a small sample of directly comparable spreadsheets which all try to solve the same task. Our analysis of the obtained spreadsheets indicates that the correctness of "bottom-line" results is not affected by the observance of the three Best Practices. However, initially correct spreadsheets with high observance of these Best Practices tend to be the ones whose later modifications yield the most correct results.
Information Leakage Neutralization for the Multi-Antenna Non-Regenerative Relay-Assisted Multi-Carrier Interference Channel
Venue: arXiv cs.IT 2012
Year: 2012

In heterogeneous dense networks where spectrum is shared, users privacy remains one of the major challenges. On a multi-antenna relay-assisted multi-carrier interference channel, each user shares the frequency and spatial resources with all other users. When the receivers are not only interested in their own signals but also in eavesdropping other users' signals, the cross talk on the frequency and spatial channels becomes information leakage. In this paper, we propose a novel secrecy rate enhancing relay strategy that utilizes both frequency and spatial resources, termed as information leakage neutralization. To this end, the relay matrix is chosen such that the effective channel from the transmitter to the colluding eavesdropper is equal to the negative of the effective channel over the relay to the colluding eavesdropper and thus the information leakage to zero. Interestingly, the optimal relay matrix in general is not block-diagonal which encourages users' encoding over the frequency channels. We proposed two information leakage neutralization strategies, namely efficient information leakage neutralization (EFFIN) and optimized information leakage neutralization (OPTIN). EFFIN provides a simple and efficient design of relay processing matrix and precoding matrices at the transmitters in the scenario of limited power and computational resources. OPTIN, despite its higher complexity, provides a better sum secrecy rate performance by optimizing the relay processing matrix and the precoding matrices jointly. The proposed methods are shown to improve the sum secrecy rates over several state-of-the-art baseline methods.
Coherence-based Partial Exact Recovery Condition for OMP/OLS
Venue: arXiv cs.IT 2012
Year: 2012

We address the exact recovery of the support of a k-sparse vector with Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS) in a noiseless setting. We consider the scenario where OMP/OLS have selected good atoms during the first l iterations (l<k) and derive a new sufficient and worst-case necessary condition for their success in k steps. Our result is based on the coherence μof the dictionary and relaxes Tropp's well-known condition μ<1/(2k-1) to the case where OMP/OLS have a partial knowledge of the support.
Predictive Control of a Permanent Magnet Synchronous Machine based on Real-Time Dynamic Optimization
Venue: arXiv cs.SY 2012
Year: 2012

A predictive control scheme for a permanent-magnet synchronous machine (PMSM) is presented. It is based on a suboptimal method for computationally efficient trajectory generation based on continuous parameterization and linear programming. The torque controller optimizes a quadratic cost consisting of control error and machine losses in real-time respecting voltage and current limitations. The multivariable controller decouples the two current components and exploits cross-coupling effects in the long-range constrained predictive control strategy. The optimization results in fast and smooth torque dynamics while inherently using field-weakening to improve the power efficiency and the current dynamics in high speed operation. The performance of the scheme is demonstrated by experimental results.
Performance Improvement by Changing Modulation Methods for Software Defined Radios
Venue: arXiv cs.OH 2012
Year: 2012

This paper describes an automatic switching of modulation method to reconfigure transceivers of Software Defined Radio (SDR) based wireless communication system. The programmable architecture of Software Radio promotes a flexible implementation of modulation methods. This flexibility also translates into adaptively, which is used here to optimize the throughput of a wireless network, operating under varying channel conditions. It is robust and efficient with processing time overhead that still allows the SDR to maintain its real-time operating objectives. This technique is studied for digital wireless communication systems. Tests and simulations using an AWGN channel show that the SNR threshold is 5dB for the case study.
Centralized Integrated Spectrum Sensing for Cognitive Radios
Venue: arXiv cs.OH 2012
Year: 2012

Spectrum sensing is the challenge for cognitive radio design and implementation, which allows the secondary user to access the primary bands without interference with primary users. Cognitive radios should decide on the best spectrum band to meet the Quality of service requirements over all available spectrum bands. This paper investigates the integrated centralized spectrum sensing techniques in multipath fading environment and the performance was analyzed with energy detection and wavelet based sensing techniques for unknown signal. Keywords: Cognitive Radio, Spectrum Sensing, Signal Detection, Primary User, Secondary User
Fingertip Detection: A Fast Method with Natural Hand
Venue: arXiv cs.CV 2012
Year: 2012

Many vision based applications have used fingertips to track or manipulate gestures in their applications. Gesture identification is a natural way to pass the signals to the machine, as the human express its feelings most of the time with hand expressions. Here a novel time efficient algorithm has been described for fingertip detection. This method is invariant to hand direction and in preprocessing it cuts only hand part from the full image, hence further computation would be much faster than processing full image. Binary silhouette of the input image is generated using HSV color space based skin filter and hand cropping done based on intensity histogram of the hand image
Modelling Multi-Trait Scale-free Networks by Optimization
Venue: arXiv cs.SI 2012
Year: 2012

Recently, one paper in Nature(Papadopoulos, 2012) raised an old debate on the origin of the scale-free property of complex networks, which focuses on whether the scale-free property origins from the optimization or not. Because the real-world complex networks often have multiple traits, any explanation on the scale-free property of complex networks should be capable of explaining the other traits as well. This paper proposed a framework which can model multi-trait scale-free networks based on optimization, and used three examples to demonstrate its effectiveness. The results suggested that the optimization is a more generalized explanation because it can not only explain the origin of the scale-free property, but also the origin of the other traits in a uniform way. This paper provides a universal method to get ideal networks for the researches such as epidemic spreading and synchronization on complex networks.
Random matrix over a DVR and LU factorization
Venue: arXiv cs.DS 2012
Year: 2012

Let R be a discrete valuation ring (DVR) and K be its fraction field. If M is a matrix over R admitting a LU decomposition, it could happen that the entries of the factors L and U do not lie in R, but just in K. Having a good control on the valuations of these entries is very important for algorithmic applications. In the paper, we prove that in average these valuations are not too large and explain how one can apply this result to provide an efficient algorithm computing a basis of a coherent sheaf over A^1 from the knowledge of its stalks.
Design and Implementation of Multistage Interconnection Networks for SoC Networks
Venue: arXiv cs.AR 2012
Year: 2012

In this paper the focus is on a family of Interconnection Networks (INs) known as Multistage Interconnection Networks (MINs). When it is exploited in Network-on-Chip (NoC) architecture designs, smaller circuit area, lower power consumption, less junctions and broader bandwidth can be achieved. Each MIN can be considered as an alternative for an NoC architecture design for its simple topology and easy scalability with low degree. This paper includes two major contributions. First, it compares the performance of seven prominent MINs (i.e. Omega, Butterfly, Flattened Butterfly, Flattened Baseline, Generalized Cube, Beneš and Clos networks) based on 45nm-CMOS technology and under different types of Synthetic and Trace-driven workloads. Second, a network called Meta-Flattened Network (MFN), was introduced that can decrease the blocking probability by means of reduction the number of hops and increase the intermediate paths between stages. This is also led into significant decrease in power consumption.
Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications
Venue: arXiv cs.CV 2012
Year: 2012

Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches utilized to fuse images from different sensors, in order to enhance visualization. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio, entropy, correlation coefficient and spatial frequency. Experimental results obtained from fusion process prove that the use of the neuro fuzzy based image fusion approach shows better performance in first two test cases while in the third test case fuzzy based image fusion technique gives better results.
Design and Implementation of Flight Visual Simulation System
Venue: arXiv cs.CY 2012
Year: 2012

The design requirement for flight visual simulation system is studied and the overall structure and development process are proposed in this paper. Through the construction of 3D scene model library and aircraft model, the rendering and interaction of visual scene are implemented. The changes of aircraft flight attitude in visual system are controlled by real-time calculation of aircraft aerodynamic and dynamic equations and flight simulation effect is enhanced by this kind of control. Several key techniques for optimizing 3D model and relative methods for large terrain modeling are explored for improving loading ability and rendering speed of the system. Experiment shows that, with specific function and performance guaranteed as a premise, the system achieves expected results, that is, precise real-time calculation of flight attitude and smooth realistic screen effect.
Comments on Proakis Analysis of the Characteristic Function of Complex Gaussian Quadratic Forms
Venue: arXiv cs.IT 2012
Year: 2012

An analysis of the characteristic function of Gaussian quadratic forms is presented in [1] to study the performance of multichannel communication systems. This technical report reviews this analysis, obtaining alternative expressions to original ones in compact matrix format.
Network delay-aware load balancing in selfish and cooperative distributed systems
Venue: arXiv cs.DC 2012
Year: 2012

We consider a request processing system composed of organizations and their servers connected by the Internet. The latency a user observes is a sum of communication delays and the time needed to handle the request on a server. The handling time depends on the server congestion, i.e. the total number of requests a server must handle. We analyze the problem of balancing the load in a network of servers in order to minimize the total observed latency. We consider both cooperative and selfish organizations (each organization aiming to minimize the latency of the locally-produced requests). The problem can be generalized to the task scheduling in a distributed cloud; or to content delivery in an organizationally-distributed CDNs. In a cooperative network, we show that the problem is polynomially solvable. We also present a distributed algorithm iteratively balancing the load. We show how to estimate the distance between the current solution and the optimum based on the amount of load exchanged by the algorithm. During the experimental evaluation, we show that the distributed algorithm is efficient, therefore it can be used in networks with dynamically changing loads. In a network of selfish organizations, we prove that the price of anarchy (the worst-case loss of performance due to selfishness) is low when the network is homogeneous and the servers are loaded (the request handling time is high compared to the communication delay). After relaxing these assumptions, we assess the loss of performance caused by the selfishness experimentally, showing that it remains low. Our results indicate that a network of servers handling requests can be efficiently managed by a distributed algorithm. Additionally, even if the network is organizationally distributed, with individual organizations optimizing performance of their requests, the network remains efficient.
Compressive Schlieren Deflectometry
Venue: arXiv cs.CV 2012
Year: 2012

Schlieren deflectometry aims at characterizing the deflections undergone by refracted incident light rays at any surface point of a transparent object. For smooth surfaces, each surface location is actually associated with a sparse deflection map (or spectrum). This paper presents a novel method to compressively acquire and reconstruct such spectra. This is achieved by altering the way deflection information is captured in a common Schlieren Deflectometer, i.e., the deflection spectra are indirectly observed by the principle of spread spectrum compressed sensing. These observations are realized optically using a 2-D Spatial Light Modulator (SLM) adjusted to the corresponding sensing basis and whose modulations encode the light deviation subsequently recorded by a CCD camera. The efficiency of this approach is demonstrated experimentally on the observation of few test objects. Further, using a simple parametrization of the deflection spectra we show that relevant key parameters can be directly computed using the measurements, avoiding full reconstruction.
Design of Experiments for Calibration of Planar Anthropomorphic Manipulators
Venue: arXiv cs.RO 2012
Year: 2012

The paper presents a novel technique for the design of optimal calibration experiments for a planar anthropomorphic manipulator with n degrees of freedom. Proposed approach for selection of manipulator configurations allows essentially improving calibration accuracy and reducing parameter identification errors. The results are illustrated by application examples that deal with typical anthropomorphic manipulators.
A modular framework for randomness extraction based on Trevisan's construction
Venue: arXiv cs.IT 2012
Year: 2012

Informally, an extractor delivers perfect randomness from a source that may be far away from the uniform distribution, yet contains some randomness. This task is a crucial ingredient of any attempt to produce perfectly random numbers---required, for instance, by cryptographic protocols, numerical simulations, or randomised computations. Trevisan's extractor raised considerable theoretical interest not only because of its data parsimony compared to other constructions, but particularly because it is secure against quantum adversaries, making it applicable to quantum key distribution. We discuss a modular, extensible and high-performance implementation of the construction based on various building blocks that can be flexibly combined to satisfy the requirements of a wide range of scenarios. Besides quantitatively analysing the properties of many combinations in practical settings, we improve previous theoretical proofs, and give explicit results for non-asymptotic cases. The self-contained description does not assume familiarity with extractors.
Problem Solving and Computational Thinking in a Learning Environment
Venue: arXiv cs.AI 2012
Year: 2012

Computational thinking is a new problem soling method named for its extensive use of computer science techniques. It synthesizes critical thinking and existing knowledge and applies them in solving complex technological problems. The term was coined by J. Wing, but the relationship between computational and critical thinking, the two modes of thiking in solving problems, has not been yet learly established. This paper aims at shedding some light into this relationship. We also present two classroom experiments performed recently at the Graduate Technological Educational Institute of Patras in Greece. The results of these experiments give a strong indication that the use of computers as a tool for problem solving enchances the students' abilities in solving real world problems involving mathematical modelling. This is also crossed by earlier findings of other researchers for the problem solving process in general (not only for mathematical problems).
Advances in Optimizing Recurrent Networks
Venue: arXiv cs.LG 2012
Year: 2012

After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modelling sequences,their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error.
Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets
Venue: arXiv cs.CE 2012
Year: 2012

Emerging interest of trading companies and hedge funds in mining social web has created new avenues for intelligent systems that make use of public opinion in driving investment decisions. It is well accepted that at high frequency trading, investors are tracking memes rising up in microblogging forums to count for the public behavior as an important feature while making short term investment decisions. We investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed Twitter sentiments for more than 4 million tweets between June 2010 and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger's Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of R-square (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA). We introduce a novel way to make use of market monitoring elements derived from public mood to retain a portfolio within limited risk state (highly improved hedging bets) during typical market conditions.
Efficient Majority-Logic Decoding of Short-Length Reed--Muller Codes at Information Positions
Venue: arXiv cs.IT 2012
Year: 2012

Short-length Reed--Muller codes under majority-logic decoding are of particular importance for efficient hardware implementations in real-time and embedded systems. This paper significantly improves Chen's two-step majority-logic decoding method for binary Reed--Muller codes $\text{RM}(r,m)$, $r \leq m/2$, if --- systematic encoding assumed --- only errors at information positions are to be corrected. Some general results on the minimal number of majority gates are presented that are particularly good for short codes. Specifically, with its importance in applications as a 3-error-correcting, self-dual code, the smallest non-trivial example, $\text{RM}(2,5)$ of dimension 16 and length 32, is investigated in detail. Further, the decoding complexity of our procedure is compared with that of Chen's decoding algorithm for various Reed--Muller codes up to length $2^{10}$.